import numpy as np
import cv2
import glob
import matplotlib.pyplot as plt
import pickle
%matplotlib inline
# Loading camera calibration
cameraCalibration = pickle.load( open('./camera_cal/cam_calib_pickle.p', 'rb' ) )
mtx, dist = map(cameraCalibration.get, ('mtx', 'dist'))
# Load perspective transformed images
persptransform_images=pickle.load(open('./camera_cal/persptransform_images.p','rb'))
print(np.array(persptransform_images).shape)
# Load transformation matrix
transMatrix = pickle.load( open('./camera_cal/perspective_transform.p', 'rb' ) )
M, Minv = map(transMatrix.get, ('M', 'Minv'))
#Load test images
test_image_files = glob.glob('./test_images/*.jpg')
print("{} Test images loaded".format(len(test_image_files)))
test_images=[]
for f in test_image_files:
t=cv2.imread(f)
t=cv2.cvtColor(np.array(t), cv2.COLOR_BGR2RGB)
test_images.append(t)
def showImages(images, cols = 4, rows = 5, figsize=(15,10), cmap=None):
imgLength = len(images)
fig, axes = plt.subplots(rows, cols, figsize=figsize)
indexes = range(cols * rows)
for ax,indx in zip(axes.flat,indexes):
image = images[indx]
if cmap == None:
ax.imshow(image)
else:
ax.imshow(image, cmap=cmap)
ax.set_title(test_image_files[indx])
#showImages(persptransform_images, 2, 4, (15, 13))
#showImages(test_images, 2, 4, (15, 13))
# Image processing pipeline
# Based on the images above, the S channel (index 2) returns the lanes more reliably
# Undistort and change color space to HLS
def undistandHLS(img, mtx=mtx,dist=dist):
undist=cv2.undistort(img,mtx,dist,None,mtx)
return cv2.cvtColor(undist,cv2.COLOR_RGB2HLS)
def sChannelImg(img):
#print(np.array(img).shape)
img=undistandHLS(img)
return img[:,:,1]
def lChannelImg(img):
#print(np.array(img).shape)
img=undistandHLS(img)
return img[:,:,2]
# This applies any threshold to an image
def thresholdimg(img, minthresh,maxthresh):
img_binary=np.zeros_like(img)
img_binary[(img>=minthresh) &(img<=maxthresh)]=1
return img_binary
def sobelimg(img, xory='x', sobel_kernel=3,minthresh=0, maxthresh=255):
if xory=='x':
ord1=0
ord2=1
else:
ord1=1
ord2=0
sobel=cv2.Sobel(img, cv2.CV_64F, ord1, ord2, ksize=sobel_kernel)
abs_sobel=np.absolute(sobel)
scaled=np.uint8(255.0*abs_sobel/np.max(abs_sobel))
return thresholdimg(scaled,minthresh, maxthresh)
def gradients(img,minthresh=0, maxthresh=255):
imgs=sChannelImg(img)
imgl=lChannelImg(img)
imgsl=imgs+imgl
sobelX = sobelimg(imgsl, 'x',3,minthresh,maxthresh)
sobelY = sobelimg(imgsl, 'y',3,minthresh,maxthresh)
combined = np.zeros_like(sobelX)
combined [(sobelX == 1) & (sobelY ==1)] = 1
return combined
def gradients(img,minthresh=0, maxthresh=255): undist=cv2.undistort(img,mtx,dist,None,mtx) img=cv2.cvtColor(undist,cv2.COLOR_RGB2HLS) imgs=img[:,:,2] imgl=img[:,:,2]
sbinary = np.zeros_like(imgs)
sbinary[(imgs >= 120) & (imgs <= 255)] = 1
lbinary = np.zeros_like(imgl)
lbinary[(imgl >= 40) & (imgl <= 255)] = 1
sobelx = cv2.Sobel(imgl, cv2.CV_64F, 1, 0)
abs_sobelx = np.absolute(sobelx)
scaled_sobel = np.uint8(255*abs_sobelx/np.max(abs_sobelx))
sxbinary = np.zeros_like(scaled_sobel)
sxbinary[(scaled_sobel >= minthresh) & (scaled_sobel <= maxthresh)] = 1
channels = 255*np.dstack(( imgl, sxbinary, imgs)).astype('uint8')
binary = np.zeros_like(sxbinary)
binary[((imgl == 1) & (imgs == 1) | (sxbinary==1))] = 1
binary = 255*np.dstack((binary,binary,binary)).astype('uint8')
return binary
# Combine perspective transform and color transform
colorandpersp_images = []
for img in test_images:
img=gradients(img,30,255)
img_size = (img.shape[1], img.shape[0])
img = cv2.warpPerspective(img, M, img_size)
colorandpersp_images.append(img)
showImages(colorandpersp_images, 2, 4, (15, 13),cmap='gray')
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
def determinelanes(binary_warped):
#binary_warped=colorandpersp_images[2]
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
midpoint = np.int(histogram.shape[0]//2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 10
# Set height of windows
window_height = np.int(binary_warped.shape[0]//nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),
(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),
(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) &
(nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
y_eval = np.max(ploty)
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
return out_img, left_fitx, right_fitx, ploty, left_curverad, right_curverad, left_fit, right_fit
def unwarpimg(img, left_fit, right_fit):
yMax = img.shape[0]
yvals = np.linspace(0, yMax - 1, yMax)
# Create an image to draw the lines on
color_warp = np.zeros_like(img).astype(np.uint8)
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, yvals]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, yvals])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
#undist = undistort(img)
#unwarp,Minv = warp(img,tobird=False)
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(img, 1, newwarp, 0.3, 0)
return result
cols=4
rows=2
fig, axes = plt.subplots(cols, rows, figsize=(40,40))
indexes = range(cols * rows)
for ax, index in zip(axes.flat, indexes):
test_image = test_images[index]
img=gradients(test_image ,10,160)
img_size = (img.shape[1], img.shape[0])
img = cv2.warpPerspective(img, M, img_size)
out_img, left_fitx, right_fitx, ploty, left_curverad, right_curverad, \
left_fit, right_fit = determinelanes(img)
#result = unwarpimg(test_image, left_fit, right_fit)
result = unwarpimg(out_img, left_fit, right_fit) # Uncomment and comment line 12
ax.imshow(result)
ax.plot(left_fitx, ploty, color='yellow') # Uncomment and comment line 12
ax.plot(right_fitx, ploty, color='yellow') # Uncomment and comment line 12
ax.text(10,100,str(round(left_curverad/1000,2))+' Km',fontsize=32, color='white')
ax.text(10,200,str(round(right_curverad/1000,2))+' Km',fontsize=32, color='white')
cols=4
rows=2
fig, axes = plt.subplots(cols, rows, figsize=(40,40))
indexes = range(cols * rows)
for ax, index in zip(axes.flat, indexes):
test_image = test_images[index]
img=gradients(test_image ,10,160)
img_size = (img.shape[1], img.shape[0])
img = cv2.warpPerspective(img, M, img_size)
out_img, left_fitx, right_fitx, ploty, left_curverad, right_curverad, \
left_fit, right_fit = determinelanes(img)
result = unwarpimg(test_image, left_fit, right_fit)
#result = unwarpimg(out_img, left_fit, right_fit) # Uncomment and comment line 12
ax.imshow(result)
#ax.plot(left_fitx, ploty, color='yellow') # Uncomment and comment line 12
#ax.plot(right_fitx, ploty, color='yellow') # Uncomment and comment line 12
ax.text(10,100,str(round(left_curverad/1000,2))+' Km',fontsize=32, color='white')
ax.text(10,200,str(round(right_curverad/1000,2))+' Km',fontsize=32, color='white')